Korean Journal of Air-Conditioning and Refrigeration Engineering (설비공학논문집)

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
권호 표지

An Experimental Study on Fault Detection and Diagnosis Method for a Water Chiller Using Bayes Classifier

베이즈 분류기를 이용한 수냉식 냉동기의 고장 진단 방법에 관한 실험적 연구

  • Lee, Heung-Ju (Graduate School of Mechanical Engineering, Kookmin University) ;
  • Chang, Young-Soo (Korea Institute of Science and Technology) ;
  • Kang, Byung-Ha (School of Mechanical and Automotive Engineering, Kookmin University)
  • 이흥주 (국민대학교 기계공학과 대학원) ;
  • 장영수 (한국과학기술연구원) ;
  • 강병하 (국민대학교 기계.자동차 공학부)
  • Published : 2008.07.10
Download PDF
⟨ Previous Next ⟩

Abstract

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. Failure modes in this study include refrigerant leakage, decrease in mass flow rate of the chilled water and cooling water, and sensor error of the cooling water inlet temperature. It is possible to detect and diagnose faults in this study by adopting FDD algorithm using only four parameters(compressor outlet temperature, chilled water inlet temperature, cooling water outlet temperature and compressor power consumption). Refrigerant leakage failure is detected at 20% of refrigerant leakage. When mass flow rate of the chilled and cooling water decrease more than 8% or 12%, FDD algorithm can detect the faults. The deviation of temperature sensor over $0.6^{\circ}C$ can be detected as fault.

Keywords

References

  1. McKellar, M. G., 1987, Failure diagnosis for a household refrigerators, MS thesis, Purdue University, Indiana, USA
  2. Grimmelius, H. T., Would, J. K. and Been, G., 1995, On-line failure diagnosis for compression refrigeration plants, Int. J. of Refrigeration, Vol. 18, No. 1, pp. 31-41 https://doi.org/10.1016/0140-7007(94)P3709-A
  3. Rossi, T. M. and Braun, J. E., 1997, A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners, HVAC&R Research, Vol. 3, No. 1, pp. 19-37 https://doi.org/10.1080/10789669.1997.10391359
  4. Han, D. and Ha, S., 2000, A study on the fault detection and diagnosis method for the air conditioner, Proceedings of the SAREK 2000 Summer Annual Conference, pp. 1408-1412
  5. Kim, M. and Kim, M., 2005, Studies on the performance variation of a variable speed vapor compression under fault and its detection and diagnosis, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 17, No. 1, pp. 47-55
  6. Fukunaga, K., 1990, Introduction to statistical pattern recognition, 2th ed., Academic Press, San Diego, pp. 131-141